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Adaptive Integration of Feature Matches into Variational Optical Flow Methods

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Computer Vision – ACCV 2012 (ACCV 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7726))

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Abstract

Despite the significant progress in terms of accuracy achieved by recent variational optical flow methods, the correct handling of large displacements still poses a severe problem for many algorithms. In particular if the motion exceeds the size of an object, standard coarse-to-fine estimation schemes fail to produce meaningful results. While the integration of point correspondences may help to overcome this limitation, such strategies often deteriorate the performance for small displacements due to false or ambiguous matches. In this paper we address the aforementioned problem by proposing an adaptive integration strategy for feature matches. The key idea of our approach is to use the matching energy of the baseline method to carefully select those locations where feature matches may potentially improve the estimation. This adaptive selection does not only reduce the runtime compared to an exhaustive search, it also improves the reliability of the estimation by identifying unnecessary and unreliable features and thus by excluding spurious matches. Results for the Middlebury benchmark and several other image sequences demonstrate that our approach succeeds in handling large displacements in such a way that the performance for small displacements is not compromised. Moreover, experiments even indicate that image sequences with small displacements can benefit from carefully selected point correspondences.

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Author information

Authors and Affiliations

  1. Institute for Visualisation and Interactive Systems, University of Stuttgart, Universitätsstraße 38, Germany

    Michael Stoll, Sebastian Volz & Andrés Bruhn

Authors
  1. Michael Stoll
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  2. Sebastian Volz
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  3. Andrés Bruhn
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, 151-744, Gwanak-gu, Seoul, Korea

    Kyoung Mu Lee

  2. Microsoft Research Asia, No. 5, Danling st., Haidian district, 100080, Beijing, P.R. China

    Yasuyuki Matsushita

  3. School of Interactive Computing, Georgia Institute of Technology, 801 Atlantic Drive, CCB 315, 30332, Atlanta, GA, USA

    James M. Rehg

  4. Institute of Automation, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Zhong Quan Cun East Road 95, Haidian District, 100 190, Beijing, P.R. China

    Zhanyi Hu

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Stoll, M., Volz, S., Bruhn, A. (2013). Adaptive Integration of Feature Matches into Variational Optical Flow Methods. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37431-9_1

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  • DOI: https://doi.org/10.1007/978-3-642-37431-9_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37430-2

  • Online ISBN: 978-3-642-37431-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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